{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Data Analysis using Datalab and BigQuery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>departure_delay</th>\n",
       "      <th>num_flights</th>\n",
       "      <th>arrival_delay_deciles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-37.0</td>\n",
       "      <td>107</td>\n",
       "      <td>[-66.0, -44.0, -41.0, -35.0, -30.0, -23.0, -17...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-36.0</td>\n",
       "      <td>139</td>\n",
       "      <td>[-74.0, -43.0, -39.0, -37.0, -32.0, -25.0, -18...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-35.0</td>\n",
       "      <td>191</td>\n",
       "      <td>[-68.0, -45.0, -40.0, -36.0, -28.0, -19.0, -14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-34.0</td>\n",
       "      <td>195</td>\n",
       "      <td>[-58.0, -44.0, -40.0, -35.0, -30.0, -25.0, -19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-33.0</td>\n",
       "      <td>227</td>\n",
       "      <td>[-59.0, -43.0, -39.0, -36.0, -32.0, -28.0, -20...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   departure_delay  num_flights  \\\n",
       "0            -37.0          107   \n",
       "1            -36.0          139   \n",
       "2            -35.0          191   \n",
       "3            -34.0          195   \n",
       "4            -33.0          227   \n",
       "\n",
       "                               arrival_delay_deciles  \n",
       "0  [-66.0, -44.0, -41.0, -35.0, -30.0, -23.0, -17...  \n",
       "1  [-74.0, -43.0, -39.0, -37.0, -32.0, -25.0, -18...  \n",
       "2  [-68.0, -45.0, -40.0, -36.0, -28.0, -19.0, -14...  \n",
       "3  [-58.0, -44.0, -40.0, -35.0, -30.0, -25.0, -19...  \n",
       "4  [-59.0, -43.0, -39.0, -36.0, -32.0, -28.0, -20...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query=\"\"\"\n",
    "SELECT\n",
    "  departure_delay,\n",
    "  COUNT(1) AS num_flights,\n",
    "  APPROX_QUANTILES(arrival_delay, 10) AS arrival_delay_deciles\n",
    "FROM\n",
    "  `bigquery-samples.airline_ontime_data.flights`\n",
    "GROUP BY\n",
    "  departure_delay\n",
    "HAVING\n",
    "  num_flights > 100\n",
    "ORDER BY\n",
    "  departure_delay ASC\n",
    "\"\"\"\n",
    "\n",
    "import google.datalab.bigquery as bq\n",
    "df = bq.Query(query).execute().result().to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>departure_delay</th>\n",
       "      <th>0%</th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>30%</th>\n",
       "      <th>40%</th>\n",
       "      <th>50%</th>\n",
       "      <th>60%</th>\n",
       "      <th>70%</th>\n",
       "      <th>80%</th>\n",
       "      <th>90%</th>\n",
       "      <th>100%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-37.0</td>\n",
       "      <td>-66.0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-36.0</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>-43.0</td>\n",
       "      <td>-39.0</td>\n",
       "      <td>-37.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-35.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-34.0</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-33.0</td>\n",
       "      <td>-59.0</td>\n",
       "      <td>-43.0</td>\n",
       "      <td>-39.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   departure_delay    0%   10%   20%   30%   40%   50%   60%   70%  80%  90%  \\\n",
       "0            -37.0 -66.0 -44.0 -41.0 -35.0 -30.0 -23.0 -17.0 -12.0 -3.0  6.0   \n",
       "1            -36.0 -74.0 -43.0 -39.0 -37.0 -32.0 -25.0 -18.0 -14.0 -7.0  2.0   \n",
       "2            -35.0 -68.0 -45.0 -40.0 -36.0 -28.0 -19.0 -14.0  -8.0 -4.0  3.0   \n",
       "3            -34.0 -58.0 -44.0 -40.0 -35.0 -30.0 -25.0 -19.0 -14.0 -8.0  2.0   \n",
       "4            -33.0 -59.0 -43.0 -39.0 -36.0 -32.0 -28.0 -20.0 -14.0 -7.0  5.0   \n",
       "\n",
       "   100%  \n",
       "0  33.0  \n",
       "1  49.0  \n",
       "2  85.0  \n",
       "3  39.0  \n",
       "4  25.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "percentiles = df['arrival_delay_deciles'].apply(pd.Series)\n",
    "percentiles = percentiles.rename(columns = lambda x : str(x*10) + \"%\")\n",
    "df = pd.concat([df['departure_delay'], percentiles], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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UNsogd6POtDHFhlJWZW/gfGXD/vyAyH48kDKJMB/X9sudRQRvgUBw1yPLMhVL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zPWhRDC1d2yeH/44Yc3fT4zM5P9+/fz0UcfWR+LjIykrOwf/Y/Ly8sJDw+3a0BhYX6EhflxrlHP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\nSPEWQgghXMz/B/dxZw2QTGuDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee058e210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "without_extremes = df.drop(['0%', '100%'], 1)\n",
    "without_extremes.plot(x='departure_delay', xlim=(-30,50), ylim=(-50,50));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge Exercise\n",
    "\n",
    "Your favorite college basketball team is playing at home and trailing by 3 points with 4 minutes left to go. Using this public dataset of [NCAA basketball play-by-data](https://bigquery.cloud.google.com/table/bigquery-public-data:ncaa_basketball), calculate the probability that your team will come from behind to win the game.\n",
    "<p>\n",
    "Hint (highlight to view)\n",
    "<p style='color:white'>\n",
    "You will need to find games where period=2, game_clock = 4, and (away_pts - home_pts) = 3.  Then, you will need to find the fraction of such games that end with home_pts > away_pts. </p>\n",
    "<p>\n",
    "If you got this easily, then for a greater challenge, repeat this exercise, but plot a graph of come-from-behind odds by time-remaining and score-margin. See https://medium.com/analyzing-ncaa-college-basketball-with-gcp/so-youre-telling-me-there-s-a-chance-e4ba0ad7f542 for inspiration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2018 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
